Criminal Persons Recognition Using Improved Feature Extraction Based Local Phase Quantization

Abstract

Facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. A major concern of facial recognition is achieving the accuracy on classification, precision, recall and F1-Score. Traditionally, numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification. But the simplified method is feature extraction by comparing the particular facial features of the images from the collected dataset namely Labeled faces in the wild (LFW) and Olivetti Research Laboratory (ORL) dataset. In this paper, the feature extraction is based on local phase quantization with directional graph features for an effective optimal path and the geometric features. Further, Person Identification based deep neural network (PI-DNN) has proposed are expected to provide a high recognition rate. Various performance metrics, such as recognition rate, classification accuracy, accuracy, precision, recall, F1-score is evaluated. The proposed method achieves highperformance values when it is compared with other existing methods. The novelty of this paper explains in understanding the various features of different types of classifiers used. It is mainly developed to recognize the human faces in the crowd, and it is also deployed for criminal identification.

A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

“…Face recognition [152] Skin lesion segmentation from dermoscopic images [153] Learning to cluster faces [154] Facial expression recognition method for identifying and recording emotion [155] Occluded face detection [156] Face anonymization with pose preservation [157] Consumer afect recognition using thermal facial ROIs [158] Criminal person recognition [159] Facial action unit detection [160] Masked face detection [161] Drunkenness face detection [162] Face detection and recognition [163] Driver drowsiness detection [164] Large-scale face clustering [165] Detection of facial action units [166] Facial expression recognition Action and activity recognition [167] Multiactor activity detection [168] One-shot video graph generation [169] Online graph depictions for tracking multiple 3D objects [170] Event stream classifcation [171] LiDAR-based 3D video object detection [172] Salient superpixel visual tracking [173] Video event recognition and elaboration from the bottom up [174] Multiobject tracking with embedded particle fow [175] Video scene graph generation [176] Video action detection [177] Multiobject tracking in autodriving [178,179] Skeleton-based action recognition [180] Video distinct object recognition by extraction of robust seeds [181] Video saliency detection [182] Close-to-real-time tracking in congested scenes Human pose detection [183] Human-object interaction detection [184] Railway driver behavior recognition system [185] Framework for object identifcation based on human local attributes…”